Constructs an n-dependent kernel-agnostic sampling distribution achieving minimax worst-case error rates for quadrature over smoothness classes on unbounded domains.
Batch Selection for Parallelisation of Bayesian Quadrature
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). Bayesian Quadrature is a probabilistic numerical integration technique that performs promisingly when compared to traditional Markov Chain Monte Carlo methods. However, in contrast to easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus far, been essentially serial in nature, selecting a single point to sample at each step of the algorithm. We deliver methods to select batches of points at each step, based upon those recently presented in the Batch Bayesian Optimisation literature. Such parallelisation significantly reduces computation time, especially when the integrand is expensive to sample.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ProEval is a proactive framework using pre-trained GPs, Bayesian quadrature, and superlevel set sampling to estimate performance and find failures in generative AI with 8-65x fewer samples than baselines.
citing papers explorer
-
Optimal Sampling for Kernel Quadrature on Unbounded Domains
Constructs an n-dependent kernel-agnostic sampling distribution achieving minimax worst-case error rates for quadrature over smoothness classes on unbounded domains.
-
ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
ProEval is a proactive framework using pre-trained GPs, Bayesian quadrature, and superlevel set sampling to estimate performance and find failures in generative AI with 8-65x fewer samples than baselines.